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Phylotype diversity within soil fungal functional groups drives ecosystem stability

Nature Ecology & Evolution volume 6, pages 900–909 (2022)Cite this article

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Abstract

Soil fungi are fundamental to plant productivity, yet their influence on the temporal stability of global terrestrial ecosystems, and their capacity to buffer plant productivity against extreme drought events, remain uncertain. Here we combined three independent global field surveys of soil fungi with a satellite-derived temporal assessment of plant productivity, and report that phylotype richness within particular fungal functional groups drives the stability of terrestrial ecosystems. The richness of fungal decomposers was consistently and positively associated with ecosystem stability worldwide, while the opposite pattern was found for the richness of fungal plant pathogens, particularly in grasslands. We further demonstrated that the richness of soil decomposers was consistently positively linked with higher resistance of plant productivity in response to extreme drought events, while that of fungal plant pathogens showed a general negative relationship with plant productivity resilience/resistance patterns. Together, our work provides evidence supporting the critical role of soil fungal diversity to secure stable plant production over time in global ecosystems, and to buffer against extreme climate events.

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Fig. 1: Relationships between soil fungal diversity and ecosystem stability.
Fig. 2: Relationships between soil fungal diversity and ecosystem stability in grasslands.
Fig. 3: Drivers of ecosystem stability.
Fig. 4: Direct and indirect drivers of ecosystem stability.
Fig. 5: Relationship between basal area of mycorrhizal association and ecosystem stability.
Fig. 6: Relationships between soil fungal diversity and ecosystem resistance and resilience to drought events.

Data availability

The raw data associated with this study are available at https://figshare.com/s/5299f4b83c1abec736fc (https://doi.org/10.6084/m9.figshare.14905236). ITS sequencing data associated with global surveys 1, 2 and 3 are available at https://figshare.com/s/9772d31625426d90778222 (https://doi.org/10.6084/m9.figshare.5923876.v1), the Short Read Archive (accession SRP043706)23 and https://figshare.com/s/5e16fa5b0475880c0fa5 (https://doi.org/10.6084/m9.figshare.19419335), respectively.

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Acknowledgements

This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 702057 (CLIMIFUN). M.D.-B. was supported by a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I). M.D-B. is also supported by a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático “01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación”) associated with the research project P20_00879 (ANDABIOMA). S.L. was supported by the National Natural Science Foundation of China (grant no. 32101491) and fellowship of China Postdoctoral Science Foundation (2021M701968). P.G.-P. was supported by a grant from the Spanish Ministry of Science and Innovation (DUALSOM, PID2020-113021RA-I00). E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT) and the Consellería de Educación, Cultura y Deporte de la Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188).

Author information

Authors and Affiliations

  1. Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region of Ministry of Education, China Three Gorges University, Yichang, China

    Shengen Liu & Dima Chen

  2. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Seville, Spain

    Shengen Liu & Manuel Delgado-Baquerizo

  3. Huitong Experimental Station of Forest Ecology, CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Shenyang, PR China

    Shengen Liu & Qingkui Wang

  4. Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, Madrid, Spain

    Pablo García-Palacios

  5. Mycology and Microbiology Center, University of Tartu, Tartu, Estonia

    Leho Tedersoo

  6. College of Science, King Saud University, Riyadh, Saudi Arabia

    Leho Tedersoo

  7. Multidisciplinary Institute for Environment Studies ‘Ramon Margalef’, University of Alicante, Alicante, Spain

    Emilio Guirado

  8. Andalusian Center for Assessment and Monitoring of Global Change (CAESCG), University of Almeria, Almeria, Spain

    Emilio Guirado

  9. Plant-Soil Interactions Group, Agroscope, Zurich, Switzerland

    Marcel G. A. van der Heijden

  10. Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland

    Marcel G. A. van der Heijden

  11. Fredericton Research and Development Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada

    Cameron Wagg

  12. School of Forestry & Landscape Architecture, Anhui Agricultural University, Hefei, China

    Qingkui Wang

  13. Hawkesbury Institute for the Environment, Western Sydney University, Richmond, New South Wales, Australia

    Juntao Wang & Brajesh K. Singh

  14. Global Centre for Land-Based Innovation, Western Sydney University, Penrith, New South Wales, Australia

    Brajesh K. Singh

  15. Unidad Asociada CSIC-UPO (BioFun), Universidad Pablo de Olavide, Seville, Spain

    Manuel Delgado-Baquerizo

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  1. Shengen Liu
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Contributions

M.D.-B. designed the study in consultation with S.L. and P.G.-P. S.L., M.D.-B., L.T. and E.G. analysed the data. S.L. and M.D.-B. wrote the first draft of the paper. P.G.-P., L.T., M.v.d.H., C.W., E.G., D.C., Q.W., J.W. and B.K.S. contributed significantly to improve subsequent drafts.

Corresponding author

Correspondence to Manuel Delgado-Baquerizo.

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Extended data

Extended Data Fig. 1 Sampling locations of three global field surveys.

A total of 673 ecosystems were included in this study.

Extended Data Fig. 2 Frequency of drought events (top) and global map of study plot locations (bottom).

The map data is equivalent to the SPEI reclassification in dry and wet events and normal years of 16 August 2018 to illustrate an example of the distribution of events.

Extended Data Fig. 3 Explained variation in ecosystem stability in global survey #1.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #1 (n = 235 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 4 Explained variation in ecosystem stability in global survey #2.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #2 (n = 351 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 5 Explained variation in ecosystem stability in global survey #3.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #3 (n = 87 ecosystems). The values in brackets after each groups present the variance explained.

Extended Data Fig. 6 Drivers of mean (a) and SD NDVI (b) in global survey #1.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (n = 235 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 7 Drivers of mean (a) and SD NDVI (b) in global survey #2.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (a,c) (n = 351 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 8 Drivers of mean (a) and SD NDVI (b) in global survey #3.

Multiple ranking regression reveal the relative effects of the most important predictors of ecosystem stability (a,c) (n = 87 ecosystems). The average parameter estimates (standardized regression coefficients) of the model predictors are shown with their associated 95% confidence intervals along with the relative importance of each predictor, expressed as the percentage of explained variance. *P < 0.05, **P < 0.01, ***P < 0.001. Soil saprobe = Soil fungal decomposers.

Extended Data Fig. 9 Fitted linear relationships between ecosystem stability and the diversity (richness) of selected functional groups of soil fungi across all ecosystems in global survey #2 (n = 351 ecosystems).

Akaike information criterion (AIC) was used to selected the best model. Significance levels of each predictor are *P < 0.05, **P < 0.01, ***P < 0.001. Grey shade indicates 95% confidence interval. Soil saprobes = soil fungal decomposers. Ecosystem stability was estimated at a resolution of 250 m×250 m. Fungal diversity is estimated at a resolution of 50 m×50 m. Plant diversity was estimated at a resolution of 110 m×110 m.

Extended Data Fig. 10 Explained variation in ecosystem stability in global survey #2.

Variation partitioning (%) of four categories of predictors (a): climate predictors (V1), soil properties and biomes (V2), fungi (fungal diversity and community composition) (V3) and plant richness and mycorrhizal association (V4) in explaining ecosystem stability, mean and SD NDVI, and ecosystem resistance and resilience to drought events in global survey #2 (n = 351 ecosystems). The values in brackets after each groups present the variance explained.

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Liu, S., García-Palacios, P., Tedersoo, L. et al. Phylotype diversity within soil fungal functional groups drives ecosystem stability. Nat Ecol Evol 6, 900–909 (2022). https://doi.org/10.1038/s41559-022-01756-5

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